The main objective of this project is to analyze YouTube data to gain insights into a content strategy for launching a successful YouTube Channel. The analysis will be conducted in four main parts:
- Summary of Project Content: Provides an overview of the project structure and objectives.
- Data Integration (Part 1): Introduces steps to upload data into Snowflake from Azure Storage, ensuring the dataset is prepared for analysis.
- Data Cleaning (Part 2): Performs data cleaning to prepare the final data table for analysis.
- Data Analysis (Part 3): Analyzes and visualizes the results obtained from data cleaning to explore YouTube trends across different countries and categories.
- Recommendations (Part 4): Utilizes insights to determine an optimal content category for launching a new YouTube channel, excluding "Music" and "Entertainment," and evaluates its potential success across different countries.
Please refer to the report in the Brief of requirements and Report folder for more information.
Overall, the project aims to provide actionable insights into YouTube trends, offering recommendations for content creation and promotion strategies in launching a new YouTube Channel. These insights can serve as a foundation for informed decision-making, helping creators maximize their channel's potential for success.
Dataset has been extracted through the Youtube API and made available on the Kaggle (https://www.kaggle.com/rsrishav/youtube-trending-video-dataset)
This dataset includes several months (from 2020-08-12 to today) of data of daily trending YouTube videos. Data is included for the IN, US, GB, DE, CA, FR, RU, BR, MX, KR, and JP regions (India, USA, Great Britain, Germany, Canada, France, Russia, Brazil, Mexico, South Korea, and, Japan respectively), with up to 200 listed trending videos per day.
(This task is from the Master of Data Science and Innovation course of University of Technology Sydney, and it is the asset of TD School)